Abstract: Systems and methods provide for Selective Tracking of Acknowledgments (STACKing) to improve buffer utilization and traffic shaping for one or more network devices. A network device can identify a first flow that corresponds to a predetermined traffic class and a predetermined congestion state. The device can determine a current window size and congestion threshold of the first flow. In response to a determination to selectively track a portion of acknowledgments of the first flow, the device can track, in main memory, information of a first portion of acknowledgments of the first flow. The device can exclude, from one or more buffers, a second portion of acknowledgments of the first flow. The device can re-generate and transmit segments corresponding to the second portion of acknowledgments at a target transmission rate based on traffic shaping policies for the predetermined traffic class and congestion state.

Abstract: In one embodiment, a device obtains audio data from one or more past conferences. A prediction model that predicts when participants of a conference will speak is generated based on the audio data from the one or more past conferences. The device uses the prediction model to predict a speech distribution for participants of a particular conference. Then, the device proactively optimizes audio parameters of the particular conference based on its predicted speech distribution.

Abstract: In one embodiment, a device determines a comprehension level for a portion of text associated with an online conversation, based in part on a topic of the portion of text. The device makes a comparison between the comprehension level for the portion of text with a comprehension level of a participant of the online conversation. The device generates adjusted text based on the portion of text, wherein the adjusted text has a comprehension level that is equal to or lower than that of the participant. The device provides the adjusted text for display to the participant.

Abstract: Techniques are for provided located-based contextual services to users by automatically creating location-based contextually relevant communication sessions that may take on a variety of forms, including contextual chats and micro-local services that are ephemeral, curated by a location owner and personalized by a user. As a user's context shifts throughout the day (from work to shopping to entertainment, etc.) the presented techniques filter content differently based on what the user may care about at a particular moment when the user is at a particular venue. The automatic creation and deletion of an ephemeral space/channel provides a dynamic application that changes its content or behavior based on a user's location. The user may control the interactivity level based on an adaptive user identifier by which a user's identity may take on different versions depending on location and time of day.

Abstract: Systems and methods provide for generating traffic class-specific congestion signatures and other machine learning models for improving network performance. In some embodiments, a network controller can receive historical traffic data captured by a plurality of network devices within a first period of time that the network devices apply one or more traffic shaping policies for a predetermined traffic class and a predetermined congestion state. The controller can generate training data sets including flows of the historical traffic data labeled as corresponding to the predetermined traffic class and predetermined congestion state. The controller can generate, based on the training data sets, traffic class-specific congestion signatures that receive input traffic data determined to correspond to the predetermined traffic class and output an indication whether the input traffic data corresponds to the predetermined congestion state.

Abstract: Techniques for associating manufacturer usage description (MUD) security profiles for Internet-of-Things (IoT) device(s) with secure access service edge (SASE) solutions, providing for automated and scalable integration of IoT devices with SASE frameworks. A MUD controller may utilize a MUD uniform resource identifier (URI) emitted by an IoT device to fetch an associated MUD file from a MUD file server associated with a manufacturer of the IoT device. The MUD controller may determine that a security recommendation included in the MUD file is to be implemented by a cloud-based security service provided by the SASE service and cause the IoT device to establish a connection with a secure internet gateway associated with the cloud-based security service. Additionally, or alternatively, the MUD file may include SASE extensions indicating manufacturer recommended cloud-based security services. Further, cloud-based security services may be implemented if local services are unavailable.

Abstract: In various embodiments, a device receives a video stream. One or more frames of the video stream include embedded metadata that is embedded directly into the one or more frames. The device extracts the embedded metadata from the one or more frames. The device makes an authentication determination regarding the video stream, based on whether the embedded metadata includes a digital signature associated with a sender of the video stream. The device controls, based on the authentication determination, presentation of the video stream to a recipient user.

Abstract: Techniques described herein relate to automatically generating standard network device configurations. In one example, one or more groups of network device configuration blocks may be obtained. An analysis of the one or more groups of network device configuration blocks may be performed, including identifying respective frequencies associated with respective network device configuration blocks of the one or more groups of network device configuration blocks. Based on the respective frequencies, one or more network device configuration blocks of the one or more groups of network device configuration blocks may be automatically aggregated into a standard network device configuration.

Abstract: Techniques for a context-aware secure access service edge (SASE) engine for generating security profile(s) associated with endpoint device(s) accessing the network and using the security profile(s) to evaluate a traffic flow from the endpoint device(s). The SASE engine may execute on an edge device of a computing resource network and may be configured to maintain a security profile database including an endpoint security profile mapping. Endpoint device(s) accessing the network may share endpoint, application, and/or user specific information with the SASE engine so that the SASE engine may generate a security profile specific to the endpoint, application, and/or user. Additionally, an enterprise network, associated with endpoint device(s) accessing the network, may provide default SASE security profile templates to the SASE engine.

Abstract: Disclosed is a system and method of providing a segment routing as a service application. The method includes receiving a configuration of an internet protocol environment. The configuration can be a layer 3 configuration of a single cloud environment or even across multiple cloud environments. The configuration defines routing, forwarding, and paths in the environment between different entities such as virtual machines. The method includes receiving a parameter associated with a workload of a tenant. The parameter can be a service level agreement (i.e., a best bandwidth available), a pathway requirement, a parameter associated with specific workload, and so forth. Based on the configuration and the parameter, the method includes generating tenant-defined layer 3 overlay segment routing rules that define how the workload of the tenant will route data in the internet protocol environment using segment routing.

Abstract: Techniques and architecture for determining compliance of a network with respect to a network design solution. The techniques may include determining a role for each network device of a network that comprises multiple network devices and determining one or more policies related to each network device with respect to other network devices of the network. The techniques may further include based at least in part on the roles for each network device and the one or more policies, determining a level of compliance of a configuration of the network with respect to a network design solution. Based at least in part on the level of compliance, a remedial action may be performed. In configurations, a machine learning model may be used.

Abstract: In one example, a logical representation of a first graph is generated. The first graph indicates a configuration of a network device in a network at a first time. The first graph includes a first node representative of a first configuration block of the network device, a second node representative of a second configuration block of the network device, and a first link that indicates, by connecting the first node and the second node, that the first configuration block is associated with the second configuration block. The logical representation of the first graph is compared to a logical representation of a second graph that indicates an actual or planned configuration of the network device at a second time subsequent to the first time. In response, one or more changes in the configuration of the network device from the first time to the second time are identified.

Abstract: Techniques described herein relate to automatically generating standard network device configurations. In one example, one or more groups of network device configuration blocks may be obtained. An analysis of the one or more groups of network device configuration blocks may be performed, including identifying respective frequencies associated with respective network device configuration blocks of the one or more groups of network device configuration blocks. Based on the respective frequencies, one or more network device configuration blocks of the one or more groups of network device configuration blocks may be automatically aggregated into a standard network device configuration.

Abstract: A method includes obtaining performance characterization values from endpoints managed by a first fog node at a first hierarchical level in a hierarchy of fog nodes. The method includes changing a first operating characteristic of the wireless network based on the performance characterization values. The first operating characteristic affects the operation of one or more of the endpoints. The method includes transmitting a portion of the performance characterization values to a second fog node at a second hierarchical level in the hierarchy of fog nodes. The method includes changing a second operating characteristic of the wireless network based on an instruction from the second fog node. The second operating characteristic affects the operation of the first fog node and/or other fog nodes at the first hierarchical level. Changing one or more of the first operating characteristic and the second operating characteristic satisfies an operating threshold for the wireless network.

Abstract: Techniques described herein relate to automatically generating standard network device configurations. In one example, one or more groups of network device configuration blocks may be obtained. An analysis of the one or more groups of network device configuration blocks may be performed, including identifying respective frequencies associated with respective network device configuration blocks of the one or more groups of network device configuration blocks. Based on the respective frequencies, one or more network device configuration blocks of the one or more groups of network device configuration blocks may be automatically aggregated into a standard network device configuration.

Abstract: In one example, a logical representation of a first graph is generated. The first graph indicates a configuration of a network device in a network at a first time. The first graph includes a first node representative of a first configuration block of the network device, a second node representative of a second configuration block of the network device, and a first link that indicates, by connecting the first node and the second node, that the first configuration block is associated with the second configuration block. The logical representation of the first graph is compared to a logical representation of a second graph that indicates an actual or planned configuration of the network device at a second time subsequent to the first time. In response, one or more changes in the configuration of the network device from the first time to the second time are identified.

Abstract: Certain aspects of the present disclosure are generally directed to version-aware service function chaining. One example method generally includes determining version information corresponding to one or more of a plurality of network functions to be performed for a packet for a service function chain (SFC) and encapsulating a service header in the packet for the SFC, the service header indicating the plurality of network functions to be performed for the packet and the version information corresponding to the one or more network functions. In certain aspects, the method also includes sending the packet to one or more service nodes for performing the plurality of network functions in accordance with the service header.

Abstract: Presented herein are methodologies to on-board and monitor Internet of Things (IoT) devices on a network. The methodology includes receiving at a server, from a plurality of IoT devices communicating over a network, data representative of external environmental factors being experienced by individual ones of the plurality of IoT devices at a predetermined location; generating, using machine learning, an aggregated model of the external environmental factors at the predetermined location; receiving, at the server, a communication indicative that a new IoT device seeks to join the network at the predetermined location; receiving, from the new IoT device, data representative of external environmental factors being experienced by the new IoT device; determining whether there is a discrepancy between the external environmental factors of the new IoT device and the aggregated model; and when there is such a discrepancy, prohibiting the new IoT device from joining the network.

Abstract: In one embodiment, a device obtains content data provided by a social media platform to a user of the social media platform. The social media platform selects the content data for the user based on a behavioral model of the user. The device maintains an artificial intelligence-based model that models associations between the content data and interaction data indicative of interactions between the user and the social media platform. The device selects, using the artificial intelligence-based model, an obfuscation action to lower an accuracy of the behavioral model of the user, based on one or more configuration parameters set by the user. The device initiates performance of the obfuscation action.

Abstract: Techniques for edge cloud identification. An indication of edge clouds is received. Each edge cloud is uniquely identifiable via an associated edge cloud identifier. A characteristic is received from each of the edge clouds. An edge cloud for communication is determined based on the characteristic. The edge cloud is communicated with using its associated edge cloud identifier.